An improved N-FINDR algorithm in implementation

Many endmember extraction algorithms have been developed for finding endmembers which are assumed to be pure signatures in the image data. One of the most widely used algorithms is the N-FINDR, developed by Winter et al. This algorithm assumes that, in L spectral dimensions, the L-dimensional volume formed by a simplex with vertices specified by purest pixels is always larger than that formed by any other combination of pixels. Despite the algorithm has been successfully used in various applications, it does not provide a mechanism to determine how many endmembers are needed. In this work, we use a recently developed concept of virtual dimensionality (VD) to determine how many endmembers need to be generated by N-FINDR. Another issue in implementing the algorithm is that N-FINDR starts with a random set of pixels generated from the data as the initial endmember set which cannot be selected by users at their discretion. Since the algorithm does not perform an exhaustive search, it is very sensitive to the selection of initial endmembers which not only can affect the algorithm convergence rate but also the final results. In order to resolve this dilemma, we use an endmember initialization algorithm (EIA) that can be used to select an appropriate set of endmembers for initialization of N-FINDR. Experiments show that, when N-FINDR is implemented in conjunction with such EIA-generated initial endmembers, the number of replacements during the course of searching process can be substantially reduced.